جستجوی مقالات مرتبط با کلیدواژه « فائو پنمن مانتیث » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Due to the low annual precipitation rate and, therefore, the existence of a weak and fragile ecosystem, arid regions are more subject to drought. Moreover, significant fluctuations in the temporal and spatial distribution of precipitation make drought forecasting in these areas a complicated task. Having a dry and fragile climate, Yazd Province has experienced numerous droughts within the past few decades. Therefore, it is highly important to monitor and forecast drought in this region. Population growth has increased water demand. Moreover, the increase in the concentration of greenhouse gases due to rising fossil fuels consumption has caused global warming and climate change, changing the hydrological cycle components including precipitation patterns (snow and rain). Therefore, climate change has changed the frequency, severity, and duration of droughts in many areas, especially in arid and semi-arid regions. So far, several indices have been developed to assess drought. This study used the RDI, which is based on precipitation (as system input) and potential evapotranspiration (as output), to estimate the drought. Furthermore, there are several methods for forecasting drought, among which projections of global climate models derived from greenhouse gas emission scenarios are quite common.

This study sought to analyze the efficiency of global climate models in predicting drought in arid regions (Yazd Synoptic Station, Yazd, Iran). To this end, 1961 to 2005 was selected as the base period, RDI values of which were imported to downscale the model (SDSM). Moreover, 2006 to 2018 was selected as the forecast period whose data were not imported to the model. Finally, the predictions of the model for the period 2006 to 2018 were compared with the observed RDI values of the same period for time scales of one, three, and six months. Accordingly, first, the climatic data collected from Yazd Synoptic Station (minimum and maximum temperature, relative humidity, sunshine hours, and wind speed) were prepared, and then the potential evapotranspiration (PET) was calculated for the period 1961 to 2018 via FAO-Penman-Monteith method. RDI value for the period 1961 to 2018 was then estimated on a monthly basis.Moreover, to predict drought through global climate models, CanESM2 global model forecasts were obtained based on the RCP8.5 greenhouse emission scenario from 2006 to 2018 for monthly precipitation, minimum temperature, maximum temperature, average temperature, sunshine hours, wind speed, and relative humidity. Using SDSM statistical downscaling model, the observed data for the period 1961 to 2005 and the projections of the CanESM2 model for the period 2006 to 2018 were downscaled. On the other hand, the mean surface temperature predictor was used to a downscale minimum and maximum temperatures and sunshine hours. Wind speed and surface-specific humidity predictors were also used to downscale wind speed and relative humidity outputs, respectively. Furthermore, to increase the accuracy, three predictors of precipitation, surface specific humidity, and mean surface temperature were used to downscale precipitation outputs. Finally, as for the high spatial and temporal variability of precipitation in arid regions, possible biases in precipitation data were corrected using the Linear Scaling bias correction method, which is based on the average difference between monthly observed time series and GCM historical simulations time series over the same period of the observed series. These differences were then applied to the future GCM simulated climate data to get bias-corrected climate variables.

The results obtained from the application of the FAO-Penman-Monteith method indicated that the variable trend presented by PET values during the study period was mostly induced by changes in wind speed fluctuations. Moreover, downscaled precipitation values showed a more significant error rate than those of the downscaled temperature outputs, probably due to the fact that precipitation values had more variability than the temperature ones, especially in arid climates. On the other hand, the application of the Linear scanning bias correction model showed that the precipitation values downscaled by SDSM for the base period (1961-2005) were overestimated compared to the real precipitation data over the same period. Therefore, the overestimation was corrected using the Linear scanning bias correction method. After correcting the probable precipitation biases and calculating the RDI index, the results suggested that the use of the Linear scanning bias correction model remarkably increased the accuracy of CanESM2 precipitation outputs.Then, the RDI was calculated by replacing the predicted precipitation and potential evapotranspiration values with the real data in the period 2006 to 2018 by measuring the potential evapotranspiration based on the data mentioned. Accordingly, R2 between real RDI values and the CanESM2-extracted forecasted RDI values for the 2006-2018 period was 0.472 for the 1-month time scale, while R2 was 0.738 and 0.762for for 3 and 6-month time scales, respectively.

The results indicated that the model presented a good performance at 3 and 6-month timescales. Moreover, considerable fluctuations in one-month precipitation values resulted in high variability of the one-month RDI time series, decreasing the model's performance at this timescale. Therefore, for projecting precipitation in arid and hyper-arid zones, a bias correction method should be applied to minimize probable biases. Furthermore, the comparison of actual droughts that occurred from 2006 to 2018 with the values ​​predicted by the model at 1, 3, and 6-month time scales showed that the CanESM2 model predicted the drought patterns with relatively good accuracy at 3 and 6-month timescales. However, as for the one-month timescale, the model presented lower efficiency than the other two time scales due to more fluctuations. Therefore, it can be concluded that GCM forecasts have a relatively acceptable efficiency for long-term drought prediction in arid climates.

Drought forecasting is of particular importance in water resources management. Drought forecasting allows planners to schedule for reducing the negative impacts of drought as well as to adapt to it. Drought prediction is more important in arid regions. Because these areas are inherently water scares and the consequences of drought in these areas are more severe. Due to the high variabilities of the temporal and spatial distribution of precipitation in these areas, the frequency of drought is higher and results in more difficulty to model and predict drought. In this study, since drought time series is nonlinear and cyclic, nonlinear autoregressive neural networks (NARs) were used to predict short-term and long-term drought in Yazd synoptic station from 2006 to 2018. Reconnaissance Drought Index (RDI) which in addition to precipitation, considers potential evapotranspiration to monitor droughts, for one, three, and six months timescales was calculated. Potential evapotranspiration was calculated using the FAO-Penman-Monteith method. The results of short-term (one month) drought prediction presented that the model provides high performance in predicting three and six months RDI values. The results of long-term (13-years) drought forecasting (without access to real drought data from 2006 to 2018) indicated that RDI values in dry months show best fit to real values ​​ in ​​ three months’ timescale. To improve the efficiency of the model in the long-term drought forecasting, long-term precipitation and potential evapotranspiration (without model access to real data from 2006 to 2018) were predicted. RDI values ​​were then calculated based on the predicted precipitation and potential evapotranspiration data. The results showed that the prediction accuracy increased in one and three months scales. Also, on six months’ timescale, RDI data were more accurately predicted in dry months.

Evapotranspiration is one of the most important components of the hydrology cycle for planning irrigation systems and assessing the impacts of climate change hydrology and correct determination is important for many studies such as hydrological balance of water, design of irrigation irrigation networks, simulation of crop yields, design, optimization of water resources, nonlinearity, inherent uncertainty, and the need for diverse climatic information in estimating evapotranspiration have been the reasons why researchers have used artificial intelligence-based approaches. In this study, to estimate accurately the daily reference evapotranspiration between 2009-2018 in Zabol city, north of Sistan and Baluchestan province,  first was used a standard FAO-Penman-Montith method and Zabol synoptic station meteorological data- the ETo reference transpiration is calculated and then presented by various scenarios of meteorological parameters including: maximum, minimum and mean temperature, maximum, minimum and mean humidity, precipitation, sunshine, wind speed and evaporation as inputs for deep learning models, Random forest and generalized linear model were attempted on a daily time scale More accurately. In estimating daily evapotranspiration in these models, 25 scenarios were selected from meteorological data combination and FAO-Penman-Monteith method was used to evaluate the models. Among the investigated scenarios, the M5 scenario (maximum, minimum and mean temperature, maximum, minimum and mean humidity, wind speed, pan evaporation) for deep learning model with minimum error (0.517) and highest correlation coefficient (0.517). 0.996 had the best performance among the above models. The deep learning model showed more accuracy and stability than other models. Hence, this study is recommended a deep learning model for estimating reference plant evapotranspiration in Sistan plain.

One of the most fundamental processes, which influence climate and weather, both global and local scales that a fact which gives it the status of agriculture, is Evapotranspiration (ET). In irrigation and water resources management, ecosystem modelers, environmental assessment and solar energy system, accurate assessment of evapotranspiration is essential. Potential evapotranspiration (ET) has commonly applied to calculate the actual evapotranspiration, which was difficult to estimate by lysimeter measurement and water balance approach under field conditions. Until now, many methods have reported to estimating ET, however, due to the availability of the observed data, it is difficult to choose the optimal method. In this study, to determination of the best potential evapotranspiration method for the Khorasan Razavi Province, three temperature-based methods, Hargreaves–Samani (HS), Hamon (HAM) and Linacre (LIN) and five radiation-based methods, Jensen-Haise (JH), Makkink (MAK), McGuinness and Bordne (MB), Abtew (ABT), and Priestley–Taylor (PT), were compared with PM at yearly scale, using long-term (11-67 years) data from 13 meteorology stations. Indicators, viz. The correlation coefficient (CC), Relative bias (BIAS), normalized root mean squared error (NRMSE) and Relative error(Re) were used to evaluate the performance of ET estimations by the above-mentioned eight methods. The results showed that the performance of the methods in ET estimation varied among regions; ETLIN overestimated ET, while others underestimated. In Dargaz and Golmakan, ETHS yielded similar estimations to that of ETPM, while, in Torbate-jam and Khaf, ETLIN showed better performances. Also in Mashhad and Gonabad, ETHAM, in Neyshabour, ETJH and in Torbat-e Heydarieh, ETABT showed better performances. But in Fariman, Kashmar, Sabzevar, Sarakhs and Quchan, all methods showed poor performance. It indicated that ETPM is acceptable for ET simulation for the yearly timescale in these areas.    ​

The accurate estimation of daily Evapotranspiration (ET) improves the efficiency of water resources management especially in areas where suffers from water scarcity. In the present study, ET was estimated using surface energy balance algorithm for land (SEBAL) and the experimental model of FAO-Penman-Monteith (FPM) and finally compared and verified with those calculated from pan evaporation method. Since many climatic factors affect the ET values, the sensitivity analysis of SEBAL inputs variables was finally cerried out to determine the key affecting parameters. In this regard, by SEBAL model and emplying the satellite data of Landsat 8 (OLI and TIRS sensors), the ET values were estimated on a daily scale for the time period 2018/07/25 to 2018/09/11. Results of SEBAL model showed that the values of SEE, RMSE and R2 indices were equal to 1.27, 0.76 and 0.77 mm /day and 0.91, 0.6 and 0.92 mm /day, while compared with those of FPM and pan evaporation methods, respectively.

One of the most important componenets of water management in farms is estimating crops’ exact amount of  evapotranspiration (water need). The FAO-Penman-Montheis (FPM) method is a standard method to evaluate other techniques which are used for easy calculation of potential evapotranspiration, when lysimeter datasheets are not available. This study was carried out based on 18 years’ climatic data of Birjand (1998-2016), to determine the best experimental method based on coefficient of evaporation pan amongst methods proposed by Cuenica, Allen and Pruitt, Snyder and Orang. Then, the results were compared with the FPM method. The best method was used to assess the capabilities of gene expression method for prediction of daily coefficient of evaporation pan, which was the major goal of this study. The best equation for calculation of daily coefficient of evaporation pan was determined using these statistical factors: coefficient of determination, root mean squared error, mean absolute error, and percent error. The results showed that in Birjand’s climatic conditions, it is best to use the equation of generated by the gene expression method with a coefficient of determination of 0.77 and mean squared error of 0.021 millimeter per day.

In water systems, precipitation is considered as input and evaporation as the output of the system. Water availability can be estemated from the relationship between these two factors. Therefore, evapotranspiration is the most important factor after precipitation in hydrological cycle. Evapotranspiration is influenced by climatic parameters such as temperature, wind, humidity and sunshine hours. In this research, changes in PET and effective climatic parameters, influencing on PET changes including temperature, wind, humidity, and solar radiation were investigated. For this purpose, PET in 14 weather stations of Yazd province were calculated using the FAO-Penman-Monteith method. Due to the lack of sunshine hours data in some stations, regeneration of the incomplete data was done by using regression method. Due to the lack of wind speed data at some stations, their reconstruction by using data from other stations was done by applying three methods of Inverse Distance Weighted, Kriging and Cokriging. After calculating potential evapotranspiration, PET data were zoned and their monthly and annual trends tested by Mann-Kendall test. Despite occurrence of climate change and increasing of temperature in 13 stations out of the 14 stations, it is expected an increase in potential evapotranspiration in past few decades, while, there is a decreasing trend in PET. Investigating on the effective parameters in potential evapotranspiration showed that wind speed has declined in the last few decades, and despite of an increase in temperature, potential evapotranspiration rate reduces in 64.3% of the stations. General trend of evapotranspiration was -0.86 in this period, which indicates a decrease in evapotranspiration in the Yazd province.